Search

An individual-based model of stochastic branching is proposed and studied, in which point particles drift in $$\bar{\mathbb {R}}_{+}:=[0,+\infty )$$ R ¯ + : = [ 0 , + ∞ ) toward the origin (edge) with unit speed, where each of them splits into two particles that instantly appear in $$\bar{\mathbb {R}}_{+}$$ R ¯ + at random positions. During their drift, the particles are subject to a random disappearance (death). The model is intended to capture the main features of the proliferation of tumor cells, in which trait $$x\in \bar{\mathbb {R}}_{+}$$ x ∈ R ¯ + of a given cell is time to its division and the death is caused by therapeutic factors. The main result of the paper is proving the existence of an honest evolution of this kind and finding a condition that involves the death rate and cell cycle distribution parameters, under which the mean size of the population remains bounded in time.

This conceptual analysis examines the implementation of a state-funded college and career readiness pilot program at a small, rural all-Black school in Illinois. Drawing from ethnographic data that dates from 2010, I offer a retrospective analysis on individual based intervention programming for college and career readiness. Concerned with how young Black high school students are constructed as problems in regards to increasing readiness, I trace policy failure through the theoretical lenses of deficit ideology (Valencia in The evolution of deficit thinking: educational thought and practice, Routledge, New York, 1997) and dysconscious racism (King in J Negro Educ 60(2):133–146, 1991). This analysis illuminates how racism ultimately undermines a-contextual efforts to increase readiness for college and career.

Individual-based modelling is an increasingly popular framework for modelling biological systems. Many of these models represent space as a lattice, thus imposing unrealistic limitations on the movement of the modelled individuals. We adapt an existing model of three competing species by using a lattice-free approach, thereby improving the realism of the spatial dynamics. We retrieve the same qualitative dynamics as the lattice-based approach. However, by facilitating a higher spatial heterogeneity and allowing for small spatial refuges to form and persist, the maintenance of coexistence is promoted, in correspondence with experimental results. We also implement a directed movement mechanism allowing individuals of different species to pursue or flee from each other. Simulations show that the effects on coexistence depend on the level of aggregation in the community: a high level of aggregation is advantageous for maintaining coexistence, whereas a low level of aggregation is disadvantageous. This agrees with experimental results, where pursuing and escaping behaviour has been observed to be advantageous only in certain circumstances.

On regulated rivers, managers must understand how drivers they can influence interact with the system to affect the health of resident fish populations. One potential way to gain insight into how these drivers interact or act in insolation is with individual based models (IBM), which can take numerous environmental drivers as inputs and mechanistically simulate their effects on the individuals in the system. This paper uses inSALMO, a spatially explicit IBM for salmon freshwater life stages, to examine how eight different drivers affect nine response variables for winter run Chinook salmon on the Sacramento River, CA. This paper examines the effects spawner numbers, spawner timing, water temperature, flow rate, turbidity, habitat cover, gravel area, and food concentration on superimposition, temperature induced egg mortality, predation, stranding, poor condition mortality, age at out-migration, length at out-migration, number of out-migrants, and juvenile size distribution. Notable results included: flow’s lack of effect on juvenile stranding and small effect on final out-migrant count, the degree to which flow affects superimposition risk, temperature having the largest effect on final juvenile out-migrant count, the interaction between predation and temperature induced egg mortality which produces a constantly decreasing out-migration count with temperature, and the level at which gravel additions would not have added benefits for superimposition mortality. While this method uses simulations, and thus will not have perfectly fidelity to the system, it is a cost-effective and quick method for gaining a mechanistically derived understanding of the complex relationships between numerous drivers and response variables.

Despite major strides in the treatment of cancer, the development of drug resistance remains a major hurdle. One strategy which has been proposed to address this is the sequential application of drug therapies where resistance to one drug induces sensitivity to another drug, a concept called collateral sensitivity. The optimal timing of drug switching in these situations, however, remains unknown.To study this, we developed a dynamical model of sequential therapy on heterogeneous tumors comprised of resistant and sensitive cells. A pair of drugs (DrugA, DrugB) are utilized and are periodically switched during therapy. Assuming resistant cells to one drug are collaterally sensitive to the opposing drug, we classified cancer cells into two groups,ARandBR, each of which is a subpopulation of cells resistant to the indicated drug and concurrently sensitive to the other, and we subsequently explored the resulting population dynamics.Specifically, based on a system of ordinary differential equations forARandBR, we determined that the optimal treatment strategy consists of two stages: an initial stage in which a chosen effective drug is utilized until a specific time point,T, and a second stage in which drugs are switched repeatedly, during which each drug is used for a relative duration (i.e.fΔt-long forDrugAand (1 –f) Δt-long forDrugBwith 0 ≤f≤ 1 and Δt≥ 0). We prove that the optimal duration of the initial stage, in which the first drug is administered,T, is shorter than the period in which it remains effective in decreasing the total population, contrary to current clinical intuition.We further analyzed the relationship between population makeup,, and the effect of each drug. We determine a critical ratio, which we term, at which the two drugs are equally effective. As the first stage of the optimal strategy is applied,changes monotonically toand then, during the second stage, remains atthereafter.Beyond our analytic results, we explored an individual based stochastic model and presented the distribution of extinction times for the classes of solutions found. Taken together, our results suggest opportunities to improve therapy scheduling in clinical oncology.

Author name disambiguation plays a very important role in individual based bibliometric analysis and has suffered from lack of information. Therefore, some have tried to leverage external web sources to obtain additional evidence with success. However, the main problem is generally the high cost of extracting data from web pages due to their diverse designs. Considering this challenge, we employed ResearchGate (RG), a social network platform for scholars presenting their publication lists in a structured way. Even though the platform might be imperfect, it can be valuable when it is used along with traditional approaches for the purpose of confirmation. To this end, in our first (retrieval) stage we applied a graph based machine learning approach, connected components (CC) and formed clusters. Then, the data crawled from RG for the same authors were combined with the CC results in stage 2. We observed that 76.40% of the clusters formed by CC were confirmed by the RG data and they accounted for 68.33% of all citations. Second, a subset was drawn from the dataset by retaining those clusters having at least 10 members to examine the details. This time we additionally employed the Google Custom Search Engine (CSE) API to access authors' web pages as a complementary tool to RG. We observed an F score of 0.95 when CC results were confirmed by RG&CSE. Almost the same success was observed when only the CC approach was applied. In addition, we observed that the publications identified and confirmed through the external sources were cited to a greater extent than those publications not found in the related external sources. Even though promising, there are still issues with the use of external sources. We have seen that many authors present only a few selected papers on the web. This hampers our procedure, making it unable to obtain the entire publication list. Missing publications affect bibliometric analysis adversely since all citation data is required. That is, if only the data confirmed via external sources is used, bibliometric indicators will be overestimated. On the other hand, our suggested methodology can potentially decrease the manual work required for individual based bibliometric analysis. The procedure may also present more reliable results by confirming cluster members derived from unsupervised grouping methods. This approach might be especially beneficial for large datasets where extensive manual work would otherwise be required.

Background Ageing can be classified in two different ways, chronological ageing and biological ageing. While chronological age is a measure of the time that has passed since birth, biological (also known as transcriptomic) ageing is defined by how time and the environment affect an individual in comparison to other individuals of the same chronological age. Recent research studies have shown that transcriptomic age is associated with certain genes, and that each of those genes has an effect size. Using these effect sizes we can calculate the transcriptomic age of an individual from their age-associated gene expression levels. The limitation of this approach is that it does not consider how these changes in gene expression affect the metabolism of individuals and hence their observable cellular phenotype. Results We propose a method based on poly-omic constraint-based models and machine learning in order to further the understanding of transcriptomic ageing. We use normalised CD4 T-cell gene expression data from peripheral blood mononuclear cells in 499 healthy individuals to create individual metabolic models. These models are then combined with a transcriptomic age predictor and chronological age to provide new insights into the differences between transcriptomic and chronological ageing. As a result, we propose a novel metabolic age predictor. Conclusions We show that our poly-omic predictors provide a more detailed analysis of transcriptomic ageing compared to gene-based approaches, and represent a basis for furthering our knowledge of the ageing mechanisms in human cells. Electronic supplementary material The online version of this article (10.1186/s12859-018-2383-z) contains supplementary material, which is available to authorized users.

Five studies investigated the relationship between individual-based relative deprivation (IRD) and prosocial behaviors. Study 1 found that income satisfaction, a concept closely related to IRD, was negatively associated with prosocial values across cultures. Study 2 found a negative association between IRD and prosocial aspirations among a sample of Chinese university students. Study 3 revealed a negative association between IRD and volunteer behaviors. In Studies 4 and 5, we found that laboratory-induced IRD decreased undergraduate students’ prosocial values and behaviors. Moreover, Study 5 also found that the tendency to prioritize self-interest over others’ mediated the effect of IRD on prosocial behaviors. Implications of these findings are discussed.

Bovine Tuberculosis (TB) is a major problem for the agricultural industry in several countries. TB can be contracted and spread by species other than cattle and this can cause a problem for disease control. In the UK and Ireland, badgers are a recognised reservoir of infection and there has been substantial discussion about potential control strategies. We present a coupling of individual based models of bovine TB in badgers and cattle, which aims to capture the key details of the natural history of the disease and of both species at approximately county scale. The model is spatially explicit it follows a very large number of cattle and badgers on a different grid size for each species and includes also winter housing. We show that the model can replicate the reported dynamics of both cattle and badger populations as well as the increasing prevalence of the disease in cattle. Parameter space used as input in simulations was swept out using Latin hypercube sampling and sensitivity analysis to model outputs was conducted using mixed effect models. By exploring a large and computationally intensive parameter space we show that of the available control strategies it is the frequency of TB testing and whether or not winter housing is practised that have the most significant effects on the number of infected cattle, with the effect of winter housing becoming stronger as farm size increases. Whether badgers were culled or not explained about 5 %, while the accuracy of the test employed to detect infected cattle explained less than 3 % of the variance in the number of infected cattle.

Many species of salmon around the world migrate to open ocean environments for multiple years and then return to their natal rivers to spawn. How exactly salmon are able to execute these long distance migrations, and the impact of environmental conditions on migration behavior, is not well understood. Individual based modeling is one tool that has been used to explore salmon migration in the ocean. Although models are usually not able to confirm whether a particular behavior is used, they can rule out some behaviors as unrealistic. An extensive review of published literature suggests that there is no universal migration behavior. Behaviors that fish use to navigate depend on where they are in the ocean relative to where they are going, as well as the ocean flows and conditions along the way. Future models of salmon migration should be flexible and at an appropriate scale to capture variable oceanographic conditions and fish responses.

In the original publication of the article, the third author name has been misspelt. The correct name is given in this Correction. The original version of this article was revised.

In many real-world scenarios, an individual accepts a new piece of information based on her intrinsic interest as well as friends' influence. However, in most of the previous works, the factor of individual's interest does not receive great attention from researchers. Here, we propose a new model which attaches importance to individual's interest including friends' influence. We formulate the problem of maximizing the acceptance of information (MAI) as: launch a seed set of acceptors to trigger a cascade such that the number of final acceptors under a time constraint T in a social network is maximized. We then prove that MAI is NP-hard, and for time $$T = 1,2$$ T = 1 , 2 , the objective function for information acceptance is sub-modular when the function for friends' influence is sub-linear in the number of friends who have accepted the information (referred to as active friends). Therefore, an approximation ratio $$(1-\frac{1}{e})$$ ( 1 - 1 e ) for MAI problem is guaranteed by the greedy algorithm. Moreover, we also prove that when the function for friends' influence is not sub-linear in the number of active friends, the objective function is not sub-modular.

International audience; The class of deterministic 'Daphnia' models treated by Diekmann et~al. (J Math Biol 61: 277--318, 2010) has a long history going back to Nisbet and Gurney (Theor Pop Biol 23: 114--135, 1983) and Diekmann et~al. (Nieuw Archief voor Wiskunde 4: 82--109, 1984). In this note, we formulate the individual based models (IBM) supposedly underlying those deterministic models. The models treat the interaction between a general size-structured consumer population ('Daphnia') and an unstructured resource ('algae'). The discrete, size and age-structured Daphnia population changes through births and deaths of its individuals and throught their aging and growth. The birth and death rates depend on the sizes of the individuals and on the concentration of the algae. The latter is supposed to be a continuous variable with a deterministic dynamics that depends on the Daphnia population. In this model setting we prove that when the Daphnia population is large, the stochastic differential equation describing the IBM can be approximated by the delay equation featured in (Diekmann et~al., l.c.).

In this article, we describe and compare two individual-based models constructed to investigate how genetic factors influence the development of phosphine resistance in lesser grain borer (R. dominica). One model is based on the simplifying assumption that resistance is conferred by alleles at a single locus, while the other is based on the more realistic assumption that resistance is conferred by alleles at two separate loci. We simulated the population dynamic of R. dominica in the absence of phosphine fumigation, and under high and low dose phosphine treatments, and found important differences between the predictions of the two models in all three cases. In the absence of fumigation, starting from the same initial frequencies of genotypes, the two models tended to different stable frequencies, although both reached Hardy–Weinberg equilibrium. The one-locus model exaggerated the equilibrium proportion of strongly resistant beetles by 3.6 times, compared to the aggregated predictions of the two-locus model. Under a low dose treatment the one-locus model overestimated the proportion of strongly resistant individuals within the population and underestimated the total population numbers compared to the two-locus model. These results show the importance of basing resistance evolution models on realistic genetics and that using oversimplified one-locus models to develop pest control strategies runs the risk of not correctly identifying tactics to minimise the incidence of pest infestation.

Early work on the setting of acceptance thresholds fornestmate recognition suggested that an overlap betweenthe odor templates used by different colonies could lead toa tradeoff between accepting nestmates and rejecting non-nestmates (Reeve 1989). In a recent paper, we pointed outan alternative approach to this problem (Johnson et al.2011). We started with a literature review that suggests thatrejection of nestmates is exceedingly rare in ants, although itdoes seem to occur in bees and wasps. Hence, ants do notseem to need to reject their own nestmates in order toexclude non-nestmates. Following this literature review,we suggested that if social insects are using gestalt mecha-nisms to generate a template of acceptable odors (reviewedby Tsutsui 2004; Martin and Drijfhout 2009), then theconcept of overlap between the cues used by different col-onies might be of little utility. The relevant question is ratherone of errors due to limited sensory acuity. We then inter-preted the rejection errors of bees from this perspective andshowed that per encounter rejection rates may be part of amore accurate colony level collective decision-makingprocess. Essentially, per encounter rejection rates can am-plify over many encounters to a more accurate level ofrecognition than would be the case for a single interaction.Couvillon et al. (2011), in their commentary on this work,argue that our results are based on false assumptions. Wedisagree. Couvillon et al. raise three issues with our work, towhich we respond below.Couvillon et al. (2011) argue that we did not considerprevious work on individual-level responses by guards(Couvillon et al. 2008). They further suggest that this workon individual-level responses undermines our model. Werecognize that individual social insects can change theirbehaviors in response to threats, and we have recentlypublished research on exactly this topic (van Wilgenburgetal. 2010).However,the roleof collective decision-makinghas been largely overlooked in the field of nestmate recog-nition, and the explicit goal of Johnson et al. was to explorethe nature and efficacy of such systems. Ultimately, thestringency of nestmate recognition is likely regulated byboth individual-level changes and collective responses, asthe two are not mutually exclusive.Nevertheless, the study highlighted by Couvillon et al.(2008) suffers from a number of shortcomings, rendering itsoverall conclusions about honey bee nestmate recognitiondubious. In their experiment, Couvillon et al. (2008) put afeeder dish in the entrance of one colony in an apiary underthe assumption that this will cause that colony to go out androb its neighbors. They inform us that they subsequentlyrecorded a 10-fold increase in fights at the entrances of theirstudy colonies along with a 20% increase in guard numbers.They then point out that the overall increase in defensive-ness that they recorded was completely accounted for by anindividual level response by guards who decreased theiracceptance rates. What Couvillon et al. have not mentionedin their commentary are the actual numbers involved. The

We have developed a spatially explicit model that simulates the interaction between fish and fishers based on past fish location, abundance and fish dispersal. We have examined four scenarios for the design and management of Marine Protected Areas (MPA) and for each we simulated fish biomass and fish catches: (1) No MPA. (2) A single MPA located at a feeding area. (3) A single MPA designed to maximise its overlap with the predominant route of fish dispersal. (4) The use of two MPAs. Each scenario was replicated with two scenarios regarding the time that fish remains within the MPA and two grid map scenarios to account for time–space effects and map/coastline characteristics. Results showed that overall closing an area increased fish biomass. However, an MPA located in the open sea for a limited time may have adverse effects on fish biomass. MPAs increased fish catches when a single large MPA or two small MPAs were located in the open sea for a limited time. The effects of time that fish remains protected in closed areas vary in combination with the spatial design: When examining time effects on the efficacy of MPAs within each scenario with an MPA located in the open sea, fish biomass was always higher in the case where fish was protected for more calendar days during each year. When comparing between different spatial designs, proximity to the coast was a more predominant factor in the efficacy of MPAs rather than time that fish was protected. The scenario that gave the highest total fish biomass was the one that covered the largest part of the migration route, despite increased edge effects. Our results suggest that it is not per se the perimeter to surface ratio that matters, but the trade-off between edge effects and maximised MPA surface in the predominant dispersal direction. Our results also have implications for the design of terrestrial reserves.

The objective of this study was to assess intervention strategies against a novel influenza epidemic through simulations of various scenarios in Sapporo city, Hokkaido, Japan. A series of interventions were examined: administration of antiviral drugs by two approaches [targeted antiviral prophylaxis (TAP) and school-age targeted antiviral prophylaxis (STAP)], school closure, restraint, and combinations of these four interventions.In order to generate a more realistic situation, we constructed an individual-based model (IBM) for the transmission of influenza in which each individual was assigned personal information on the basis of the National Census and Employment Status Survey of Sapporo city. In addition, data on high-risk casual contact groups commuting in crowded trains and buses were obtained from a census on transportation modes and introduced into the model. Observational data from previous pandemics were used for the epidemiological parameters.Both TAP and STAP interventions were highly effective in suppressing the spread of infection during the early period of an outbreak, but STAP was inferior to TAP in terms of the ripple effect of the administration of antiviral drugs. School closure and restraint were able to bring about a delay in the peak of infection. The combination of TAP, school closure, and restraint interventions were highly effective in decreasing the total number of patients and shortening the epidemic period.Based on the simulation results, we recommend implementing TAP together with both school closure and restraint as strategies against a future novel influenza outbreak.

This study is aimed at the evaluation of countermeasures in terms of the total number of patients and deaths, the number of vaccine doses used, and the time for eradication as examined through simulations of the outbreak of smallpox following a bioterrorist attack in the center of Okayama City. Three countermeasures were considered: traced vaccination (TV), mass vaccination (MV), and school closure. An individual-based model was adopted, in which every individual is assigned their own personal information, behavioral pattern, and interactions among social settings to simulate the situation on a realistic basis. The influence of residual immunity obtained by past vaccination reflected age-dependent immunity to smallpox in Japan. MV performed within a 2-day period at the same time in 10 school districts had the highest effectiveness in reducing the total number of patients and deaths among all simulations. Performing both TV and MV simultaneously was much more effective than performing TV or MV individually. The decrease in the number of patients with TV or MV in combination with school closure was faster than that by TV or MV without school closure. According to the simulations results, it was advisable to carry out MV, or both TV and MV, simultaneously with school closure as countermeasures against a smallpox epidemic initiated by a bioterrorist attack.

Complex spatio-temporal patterns of cell clusters were observed in colonies of chemotactic bacteria such as Escherichia coli or Sallmonella typhimurium. The production of a potent chemoattractant by the bacteria themselves as a reaction to certain nutrients is the essential factor for this pattern formation. Additional collective dynamics, such as collective translation and rotation of bacterial clusters were reported from experiments on bacterial colonies. Motivated by this observations we suggest a simple model for the description of bacterial colonies using the concept of active Brownian particles. Individual based models represent an interesting alternative to the usually employed mean field chemotaxis-diffusion equations (Keller-Segel model) as they allow us to study the macroscopic pattern formation of the colony, the collective dynamics of bacterial ensembles, as well as the microscopic dynamics of individual cells. In this paper we derive microscopic model equations from basic assumptions about bacterial dynamics, discuss the parameter choice by comparison with biological data and analyse the macroscopic and microscopic dynamics of the system. Finally we extend the model by a velocity-alignment (swarming) interaction which leads to novel collective dynamics in the system.

Micro-organisms play a central role in every ecosystem and in the global biomass cycle. They are strongly involved in many fields of human interest, from medicine to the food industry and waste control. Nevertheless, most micro-organisms remain almost unknown, and nearly 99% of them have not yet been successfully cultured in vitro. Therefore, new approaches and new tools must be eveloped in order to understand the collective behaviour of microbial communities in any natural or artificial setting. In particular, theoretical and practical methodologies to deal with such systems at a mesoscopic level of description (covering the range from 100 to 108 cells) are required. Individualbased modelling (IBM) has become a widely used tool for describing complex systems made up of autonomous entities, such as ecosystems and social networks. Individual-based models (IBMs) provide some advantages over the traditional whole-population models: (a) they are bottom-up approaches, so they describe the behaviour of a system as a whole by establishing procedural rules for the individuals and for their interactions, and thus allow more realistic assumptions for the model of the individuals than population models do; (b) they permit the introduction of randomness and individual variability, so they can reproduce the diversity found in real systems; and (c) they can account for individual adaptive behaviour to their environmental conditions, so the evolution of the whole system arises from the dynamics that govern individuals in their pursuit of optimal fitness. However, they also present some drawbacks: they lack the clarity of continuous models and may easily become rambling, which makes them difficult to analyse and communicate. All in all, IBMs supply a holistic description of microbial systems and their emerging properties. They are specifically appropriate to deal with microbial communities in non-steady states, and spatially explicit IBMs are particularly appropriate to study laboratory and natural microbiological systems with spatial heterogeneity. In this paper, we review IBM methodology applied to microbiology. We also present some results obtained from the application of Individual Discrete Simulations, an IBM of ours, to the study of bacterial communities, yeast cultures and Plasmodium falciparum-infected erythrocytes in vitro cultures of Plasmodium falciparum-infected erythrocytes

Blood tests allow doctors to check for certain diseases and conditions. However, using a syringe to extract the blood can be deemed invasive, slightly painful, and its analysis time consuming. In this paper, we propose a new non-invasive system to detect the health status (Healthy or Diseased) of an individual based on facial block texture features extracted using the Gabor filter. Our system first uses a non-invasive capture device to collect facial images. Next, four facial blocks are located on these images to represent them. Afterwards, each facial block is convolved with a Gabor filter bank to calculate its texture value. Classification is finally performed using K-Nearest Neighbor and Support Vector Machines via a Library for Support Vector Machines (with four kernel functions). The system was tested on a dataset consisting of 100 Healthy and 100 Diseased (with 13 forms of illnesses) samples. Experimental results show that the proposed system can detect the health status with an accuracy of 93 %, a sensitivity of 94 %, a specificity of 92 %, using a combination of the Gabor filters and facial blocks.

Proteomics can help to define and identify patients that will most benefit from the increasing number of molecular-targeted agents. In particular, reverse phase protein microarray provides detailed information about the state of the cellular 'circuitry' from small samples such as patient biopsy specimens. This review discusses the clinical applications of this emerging technology and how it could be used to help guide individualized treatment approaches. Patient-tailored medicine can be defined as the selection of specific therapeutics to treat disease in a particular individual based on genetic, genomic or proteomic information. While individualized treatments have been used in medicine for years, advances in cancer treatment have now generated a need to more precisely define and identify those patients who will derive the most benefit from new-targeted agents. Cellular signaling pathways are a protein-based network, and the intended drug effect is to disrupt aberrant protein phosphorylation-based enzymatic activity and epigenetic phenomena. Pharmacoproteomics, or the tailoring of therapy based on proteomic knowledge, will begin to take a central role in this process. A new type of protein array platform, the reverse-phase protein microarray, shows potential for providing detailed information about the state of the cellular 'circuitry' from small samples such as patient biopsy specimens. Measurements of hundreds of specific phosphorylated proteins that span large classes of important signaling pathways can be obtained at once from only a few thousand cells. Clinical implementation of these new proteomic tools to aid the clinical, medical and surgical oncologist in making decisions about patient care will now require thoughtful communication between practicing clinicians and research scientists.

Scyphomedusae are ubiquitous in marine and estuarine systems, where they frequently play an important role in trophodynamics. Many scyphomedusae are cruising predators, and feeding rates depend, in part, on swimming behavior. Yet, no model of medusa swimming exists. An individual-based correlated random walk (CRW) model of medusa swimming behavior in three dimensions was developed. The model was validated using a previously published dataset of the swimming of 19 Chrysaora quinquecirrha (Desor, 1848) medusae that were observed in the presence or absence of zooplankton prey in laboratory mesocosms in August–October 1998 (Matanoski et al. in Mar Biol 139:191–200, 2001). In the presence of prey, medusae swam at a constant moderate rate in looping trajectories. In the absence of prey, medusae alternated periods of slow and fast swimming in more linear trajectories. In the model, looping trajectories were reproduced only when changes in movement by a medusa were oriented to its current position and orientation; more linear trajectories were reproduced by movement oriented to a fixed framework. This suggests that medusae change from swimming behavior oriented to local stimuli (e.g., contact with prey) to long-range stimuli (e.g., gravity) depending on the availability of prey. The model reproduced cyclical changes in swimming speeds by medusae in the absence of prey by simulating switching in the behavior controlling the strength of swimming bell pulsations using a probabilistic function. Model results also demonstrated that medusae tend to swim toward the surface, avoid contact with the bottom, increase time spent in prey patches if they alter swimming patterns in the presence of prey, and exhibit significant periodicities in swimming patterns that are the result of deterministic behavior. The model will permit the simulation of the complex behavior of medusae.

Lifestyle modifications, including dietary and physical activity, are treatments for many chronic health conditions. Therefore, there is continued interest in improving the quantity and quality of nutrition information provided to the patient by the physician. This paper reviews the evidence to support motivational interviewing and other similar strategies for nutrition communications. Limited but positive data were found.

A computer model of the sockeye salmon (Onchorhynchus nerka) population from Dal'neye Lake (Kamchatka) based on the results of long-term research was developed. As opposed to an earlier published model [4], the stochastic approach and individual-based method were used in the present model. The application of such techniques in the model has enabled study of the dynamics of the genetic structure of the population under the effect of varying environments, both in the sea and in the fresh-water period of the sockeye's life, and discovery of patterns of transition from the cyclic fluctuation mode to the mode of chaotic change of number.

The possibility of chaos control in biological systems has been stimulated by recent advances in the study of heart and brain tissue dynamics. More recently, some authors have conjectured that such a method might be applied to population dynamics and even play a nontrivial evolutionary role in ecology. In this paper we explore this idea by means of both mathematical and individual-based simulation models. Because of the intrinsic noise linked to individual behavior, controlling a noisy system becomes more difficult but, as shown here, it is a feasible task allowed to be experimentally tested.

General response patterns of fish populations tostress, originally proposed by Colby for fisheriesrehabilitation and later adapted by Munkittrick forcontaminants, were evaluated using an individual-basedsimulation model. General response patterns relatechanges in population-level variables to the type ofstress. The model follows the daily growth,mortality, and spawning of individual yellow perch andwalleye through their lifetime, and was corroboratedusing Oneida Lake data. Two versions of the model wereused: population (yellow perch only) and community(dynamic predation on yellow perch by walleye). Eightstresses were imposed on the population and communityversions of the model and 100-year simulations wereperformed. Response patterns were defined by changesin predicted yellow perch mean population abundance,mean age of adults, and mean adult growth (representedby mean length at age-7). Proposed response patternswere similar to those predicted using the populationversion of the model. Simulations using the communityversion of the model distorted the response patterns,either causing amplification, dampening, or reversalof many of the patterns. Predicted response patternsbecame unique when additional variables were included.Our model results suggest that caution is appropriatein interpreting general response patterns based onmean age, or when the population of interest plays amajor role in a relatively simple food web. The responsepattern approach may be better at identifying the lifestage impacted rather than the mechanism of the stress.

Background Streptococcus pneumoniae is a major cause of morbidity and mortality worldwide, but also a common colonizer of the upper respiratory tract. The emergence and spread of antibiotic resistant pneumococcal strains has threatened effective therapy. The long-term effects of measures aiming to limit pneumococcal spread are poorly understood. Computational modeling makes it possible to conduct virtual experiments that are impractical to perform in real life and thereby allows a more full understanding of pneumococcal epidemiology and control efforts. Methods We have developed a contact network model to evaluate the efficacy of interventions aiming to control pneumococcal transmission. Demographic data from Sweden during the mid-2000s were employed. Analyses of the model's parameters were conducted to elucidate key determinants of pneumococcal spread. Also, scenario simulations were performed to assess candidate control measures. Results The model made good predictions of previous findings where a correlation has been found between age and pneumococcal carriage. Of the parameters tested, group size in day-care centers was shown to be one of the most important factors for pneumococcal transmission. Consistent results were generated from the scenario simulations. Conclusion We recommend, based on the model predictions, that strategies to control pneumococcal disease and organism transmission should include reducing the group size in day-care centers.

Public health applications using geographic information system (GIS) technology are steadily increasing. Many of these rely on the ability to locate where people live with respect to areas of exposure from environmental contaminants. Automated geocoding is a method used to assign geographic coordinates to an individual based on their street address. This method often relies on street centerline files as a geographic reference. Such a process introduces positional error in the geocoded point. Our study evaluated the positional error caused during automated geocoding of residential addresses and how this error varies between population densities. We also evaluated an alternative method of geocoding using residential property parcel data. Positional error was determined for 3,000 residential addresses using the distance between each geocoded point and its true location as determined with aerial imagery. Error was found to increase as population density decreased. In rural areas of an upstate New York study area, 95 percent of the addresses geocoded to within 2,872 m of their true location. Suburban areas revealed less error where 95 percent of the addresses geocoded to within 421 m. Urban areas demonstrated the least error where 95 percent of the addresses geocoded to within 152 m of their true location. As an alternative to using street centerline files for geocoding, we used residential property parcel points to locate the addresses. In the rural areas, 95 percent of the parcel points were within 195 m of the true location. In suburban areas, this distance was 39 m while in urban areas 95 percent of the parcel points were within 21 m of the true location. Researchers need to determine if the level of error caused by a chosen method of geocoding may affect the results of their project. As an alternative method, property data can be used for geocoding addresses if the error caused by traditional methods is found to be unacceptable.